Thank your feedback, questions, and critiques on our #COVID19 model. We’ve updated our FAQ, and are working to incorporate more feedback into our model and affiliated resources, including our Estimation Updates blog.🧵The following is a thread addressing recently asked questions:
What does our model say about expected new #COVID19 cases?
We are forecasting surges of new cases in Kansas, Missouri, New Jersey, Virginia, and West Virginia in the next week or two.
We currently projects surges in deaths on Jan 1 (if 95% public masking is not adhered) in these states: AL, AZ, AK, CA, DE, CO, KS, IN, IA, MD, MT, NE, NM, NV, NY, NH, NC, OH, OK, OR, PA, RI, SD, TN, UT, VA, WA.
Has mask use in the United States increased?
According to @premisedata, we saw mask use rise in the United States between April and mid-July, but start to decline in late-July/early-August.
Yes. Mask use rose in the US (per @premisedata) over June and July at exactly the same time R0 fell. Mask usage sits at 45% in the US compared to 60% globally.
Governments around the world show no appetite for re-imposing sweeping lockdowns because of its economic impacts. We believe a focus on universal masking will help save lives and the economy.
Where does your seasonality assumption come from?
So far, in our research, COVID-19 transmission correlates strongly with pneumonia seasonality.
What is the predictive validity of our #COVID19 forecasts? AKA: How do our forecasts compare to actuality?
We have 12 week median absolute percent errors from models in June, via @JosephRFriedman:
We have been forecasting four months out to give governments, policymakers, and healthcare administrators sufficient time to plan for the pandemic. They have asked us to share projections at least 4 months out.
Are you forecasting certainty?
No! You can observe our uncertainty intervals by selecting that option within each chart in our viz tool. We strongly recommend policymakers draw from a variety of models for planning purposes.
What are important assumptions of your model?
We’re going to answer this in four parts.
Important Assumption 1⃣: COVID-19 follows seasonality patterns similar to pneumonia. We've seen clear indications of seasonality from South American countries such as Chile and Argentina that have just emerged from their winter.
Important Assumption 2⃣: The “Current projection” scenario assumes that social distancing mandates will continue to be lifted, but will be re-imposed for six weeks if daily death rates reach 8 per million.
Important Assumption 3⃣: The “Mandates easing” scenario assumes that social distancing mandates will continue to be lifted and will not be re-imposed.
Important Assumption 4⃣: The “Universal masks” scenario assumes that mask wearing will reach 95% in 7 days, and social distancing mandates will continue to ease, but will be re-imposed for six weeks if daily death rates reach 8 per million.
Why do our #COVID19 forecasts show significant digits?
We report the projections our model produces with as much precision as possible and without interpretation. Rounding numbers can be helpful, which we do ourselves in press releases, newsletters, and social media posts.
Why aren’t the model’s details and assumptions more readily available?
2⃣ We are working very quickly to produce projections that can inform the policy discussion in a timely fashion, as requested by many key stakeholders; updates to the methodology are cataloged here: healthdata.org/covid/updates/…
Despite impressive achievements made to test and treat #HIV/#AIDS, there are still significant inequities in access to care globally. Investment is key to addressing these gaps in care. #WorldAIDSDay
According to the Global Burden of Disease (#GBDstudy): Since the 1980s more than 30 million people have died from #AIDS-related illnesses
863,000 #HIV-related deaths occurred in 2019 36.8 million people were living with HIV in 2019
70.7% of people living with HIV in 2019 lived in sub-Saharan Africa
All US states struggled during the pandemic, but were outcomes across states equal? Not according to analysis from @UW’s IHME examining state-by-state comparisons of health outcomes, education loss, & economic performance from Jan 1, 2020-July 31, 2022 🧵 bit.ly/COVID_By_State
The research examined how 5 🔑 areas affected each state’s pandemic performance:
-Social, racial & economic inequities
-Health care capacity
-Political influence
-COVID-19 mandates
-Economic & educational trade-offs
Did your state’s government make use of healthcare services for COVID-19 vaccinations?
Among states that voted Democrat in 2020, states with stronger health systems had the highest rates of vaccine uptake, but not among states that voted Republican.
A previous COVID infection may result in natural immunity against severe disease (hospitalization & death) for all variants (≥ 88% at 10 months post-infection).
This is on par with 2 doses of mRNA vaccines (@moderna_tx, @pfizer), finds new IHME study. bit.ly/COVIDReinfecti…
As the most comprehensive analysis to provide evidence on natural immunity protection by COVID-19 variants, the study includes data for ancestral, Alpha, Delta, and Omicron BA.1 variants. The study did not include data on infection from Omicron XBB and its sublineages.
Natural immunity from COVID-19 infection was seen to wane and vary by variant infection. For instance, past infection with pre-Omicron variants provided substantially reduced natural immunity against reinfection with Omicron BA.1.
The global burden of cancer in children is often overlooked despite being a distinct subgroup with unique epidemiology, clinical care needs, & societal impact. For International Childhood #Cancer Day, we want to address the burden inequities that exist around the world #ICCD2023
Childhood cancer (affecting ages 0-19) was the #9 leading cause of childhood disease burden globally in 2017. Children in low-income places are most impacted: 82% of global childhood cancer DALYs occurred in low & middle socio-demographic Index locations.
»healthdata.org/research-artic…
More than 11 MILLION YEARS of healthy life were estimated to be lost due to childhood cancer in 2017. Collectively childhood cancers were the #6 biggest contributor to total cancer burden when compared to individual cancers in adults (such as lung, liver, stomach, colon & breast)
What we're expecting in #China:
- Major #omicron epidemic in coming months
- According to our reference scenario, we expect 323,000 total deaths by 4/1/23
- Infections to peak around 4/1, but we expect number of susceptible individuals to sustain transmission months after April
Although there is a high #vaccination rate, there's comparatively low effectiveness of the vaccines used in #China against Omicron & the large gap since vaccination for many individuals means that 80% of the population is susceptible to #Omicron infection.
💡What are #ExcessDeaths? It’s a measure comparing the trends of deaths in previous years vs. deaths during the #COVID19 pandemic. The abnormal spike in deaths during 2020 and 2021 → excess deaths